It Takes a Whole Startup to Raise a Data Scientist

The other day, I read a blog post—an extremely amusing one, in all fairness—mocking the startup world. I’m sure it has ended up on your Twitter feed; it uses a certain swear word in every line. Yup, this one.

It got me thinking, which can only mean trouble ;). Seriously, it did get me thinking: yes, there are some perks and attitudes in startups that lend themselves to mockery and some personalities who can be easily caricatured. But if I were you I wouldn’t throw the baby out with the bathwater, not just yet. Wait until you read this blog post, at least.

So, the Office Manager Wants to Become a Data Scientist, Say Whaaat?

Besides those aspects in that post, there are some things in this mad world of tech startups that are worth praising, such as a mindset of continuous development. That may sound really pompous, but it just means a culture of promoting learning and professional growth. Nah, that still sounds out-there and pretentious. Let’s try that again.

Since I have been at ShuttleCloud I have been surrounded by an environment that encourages the acquisition of knowledge. Hmm… getting closer, but not there yet…

And you know what? That attitude rubs off. It spreads through the whole team and, without realizing it, you start incorporating it in your own approach.

So, when I overheard a conversation about data science at the office, it got me curious, given my background in research. After loads of reading to get the feel of it, it only felt natural to get into data science myself.

By the way, for a newbie in this world, it can be quite overwhelming.

Let’s learn about Big Data FrameWorks: umm… Hadoop, Spark, Storm? I’ll leave that for later. Let’s check out databases instead: SQL, NoSQL, graph databases… Ok, ok, let’s not panic. What about data visualization? There’s Tableau, D3.js… Oh dear, let’s close that door for now. I’m going to install Linux, so which distribution should I choose? They must at least agree on which programming language to use for Data Science, right? Ha! Take your pick: Python or R. And that’s just the beginning. There’s Scala for Spark, Java for Hadoop, Cypher for Neo4j, and so on and so forth. Welcome to the programming Tower of Babel! 😉

It Takes a Whole Startup to Raise a Data Scientist

When I mentioned it to the team, they might have thought, “Oh dear, the office manager’ has lost her marbles,” but they’ve been really supportive ever since!

Take our CEO Ed’s encouragement, and how he let me reschedule my work hours to accommodate master lessons. Or take Félix, the DOE, pointing me in the right direction and suggesting which books and MOOCs to tackle first. Or take Santi, the marketing manager, who offered to cover for me at office meetups—or Cris, our product manager, who sent me links to interesting meetups—or Maykel, our own DevOps engineer, who offered me tutoring in Linux and Python.

P.S: And for those I have not mentioned here, don’t worry. I have plans for you as well. Any Spark expert in the house? ;P

Don’t they say it takes a village to raise a child? Well in my case, it takes a whole startup to raise a data scientist.

So Do we Chuck the Baby out with the Bathwater?

Here you have it. Besides the ping pong table, the beer fridge, and the parties—which by the way are a lot of fun and I wouldn’t change any of it for the world—working in a tech startup has other serious perks worth highlighting. It takes a certain kind of person to thrive in this world, and it takes the right attitude to cope with its demands. Combine that with the right culture to encourage learning and, moreover, provide people the means to do it, and BOOM! The right recipe for a really inspiring environment.

And yes, a job is a job, no matter how many ribbons you put on it, and of course, any culture has its drawbacks and pitfalls, but I’d choose the ribbons and the supportive, pro-development culture every time, hands down.

So what are you going to do with that baby? Do you keep it?

I let you decide that one for yourself because I have to go—I have 7 books to read, 3 MOOCs to complete, I have to read the 100th post about how to become a data scientist, go through the masters notes, work out why that annoying error in Python keeps popping up when I try to run the gradient descent function, read some articles about Bayesian inference and…..breathe.